Abstract: A technology is described for automating deployment of a machine learning model. An example method may include receiving, via a graphical user interface, credentials for connecting to a data store containing a plurality of datasets and connecting to the data store using the credentials. A selection of a target metric to predict using the machine learning model can be received, via the graphical user interface, and datasets included in the plurality of datasets that correlate to the target metric can be identified by analyzing the datasets to identify an association between the target metric and data contained within the datasets. The datasets can be input to the machine learning model to train the machine learning model to generate predictions of the target metric, and the machine learning model can be deployed to computing resources in a service provider environment to generate predictions associated with the target metric.

Abstract: The present application is directed to a system and method for law enforcement incident reporting. More particularly, a mobile application is provided with multiple modules related to the law enforcement incident reporting procedures for vehicle-vehicle, vehicle-pedestrian, and vehicle-property collisions. The system and method of the present application can be used by law enforcement (LE) patrol officers to accurately and quickly document LE incidents and traffic collision reports in a fraction of time compared to the traditional reporting styles of using pen and paper. The system and method of the present application is primarily used as a mobile application and is compatible with both Android and iOS devices including smartphones, tablets, desktops, laptops, hand-held computers, etc. Once incidents/collisions are documented, LE officers can edit, store and send their respective reports.

Abstract: The present application is directed to a system and method for law enforcement incident reporting. More particularly, a mobile application is provided with multiple modules related to the law enforcement incident reporting procedures for vehicle-vehicle, vehicle-pedestrian, and vehicle-property collisions. The system and method of the present application can be used by law enforcement (LE) patrol officers to accurately and quickly document LE incidents and traffic collision reports in a fraction of time compared to the traditional reporting styles of using pen and paper. The system and method of the present application is primarily used as a mobile application and is compatible with both Android and iOS devices including smartphones, tablets, desktops, laptops, hand-held computers, etc. Once incidents/collisions are documented, LE officers can edit, store and send their respective reports.

Abstract: A technology is described for automating deployment of a machine learning model. An example method may include receiving, via a graphical user interface, credentials for connecting to a data store containing a plurality of datasets and connecting to the data store using the credentials. A selection of a target metric to predict using the machine learning model can be received, via the graphical user interface, and datasets included in the plurality of datasets that correlate to the target metric can be identified by analyzing the datasets to identify an association between the target metric and data contained within the datasets. The datasets can be input to the machine learning model to train the machine learning model to generate predictions of the target metric, and the machine learning model can be deployed to computing resources in a service provider environment to generate predictions associated with the target metric.